Machine Learning (stat)

Papers filed under stat.ML on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

Search paper metadata (including unsummarized papers)

7,681 to 7,740 of 9,704

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Meng Tang, Yimin Liu, Louis J. Durlofsky
cs.LGphysics.comp-phstat.MLarXiv:1908.05823v12019
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey +2
cs.LGcs.ROmath.OCarXiv:2006.12466v12020
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud +1
stat.MLcs.LGarXiv:1906.02735v62019
FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference
Gatien Séguy, Thomas Rodet
cs.LGcs.AIstat.MLarXiv:2609.29216v12026
Equivariant Manifold Flows
Isay Katsman, Aaron Lou, Derek Lim +3
stat.MLcs.LGmath.DGarXiv:2107.08596v22021
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
cs.LGstat.MLarXiv:1907.04275v32019
Wasserstein Weisfeiler-Lehman Graph Kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López +2
cs.LGq-bio.MNstat.MLarXiv:1906.01277v22019
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
Koustuv Sinha, Shagun Sodhani, Jin Dong +2
cs.LGcs.CLcs.LOarXiv:1908.06177v22019
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi +2
cs.LGstat.MLarXiv:1809.02925v22018
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Jannik Kossen, Neil Band, Clare Lyle +3
cs.LGstat.MLarXiv:2106.02584v22021
Auditing Black-box Models for Indirect Influence
Philip Adler, Casey Falk, Sorelle A. Friedler +4
stat.MLcs.LGarXiv:1602.07043v22016
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
Alexandr A. Kalinin, Gerald A. Higgins, Narathip Reamaroon +5
q-bio.QMcs.LGstat.MLarXiv:1801.08570v22018
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
Zai Yang, Lihua Xie
cs.ITstat.MLarXiv:1407.2490v22014
SLAYER: Spike Layer Error Reassignment in Time
Sumit Bam Shrestha, Garrick Orchard
cs.NEcs.LGstat.MLarXiv:1810.08646v12018
Gaussian Linear Functional Manifold Method for Massive Point Cloud Data
Hong Zhao, Tonglin Zhang, Baijian Yang +2
cs.CVcs.LGstat.MLarXiv:2609.05744v12026
The Optimal Sample Complexity of Multiclass and List Learning
Chirag Pabbaraju
cs.LGstat.MLarXiv:2604.24749v42026
Inferring clonal evolution of tumors from single nucleotide somatic mutations
Wei Jiao, Shankar Vembu, Amit G. Deshwar +2
cs.LGq-bio.PEq-bio.QMarXiv:1210.3384v42012
Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification
Ansgar Steland
math.STstat.MLarXiv:2609.26652v12026
Statistical Topic Models for Multi-Label Document Classification
Timothy N. Rubin, America Chambers, Padhraic Smyth +1
stat.MLcs.LGarXiv:1107.2462v22011
Optimization Methods for Large-Scale Machine Learning
Léon Bottou, Frank E. Curtis, Jorge Nocedal
stat.MLcs.LGmath.OCarXiv:1606.04838v32016
FreDF: Learning to Forecast in the Frequency Domain
Hao Wang, Licheng Pan, Zhichao Chen +6
cs.LGcs.AIstat.AParXiv:2402.02399v22024
Stream Graphs and Link Streams for the Modeling of Interactions over Time
Matthieu Latapy, Tiphaine Viard, Clémence Magnien
cs.SIcs.DMcs.DSarXiv:1710.04073v12017
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
cs.LGcs.AIstat.MLarXiv:1906.02589v12019
Graph Neural Networks in TensorFlow and Keras with Spektral
Daniele Grattarola, Cesare Alippi
cs.LGstat.MLarXiv:2006.12138v12020
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo +2
cs.LGstat.MLarXiv:1901.06523v72019
Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu +2
cs.LGcs.AIstat.MLarXiv:2210.02747v22022
Visual Causal Feature Learning
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
stat.MLcs.AIcs.CVarXiv:1412.2309v22014
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
cs.CVcs.LGstat.MLarXiv:2003.08802v12020
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
Shikuang Deng, Shi Gu
cs.NEstat.MLarXiv:2103.00476v12021
Sure independence screening in generalized linear models with NP-dimensionality
Jianqing Fan, Rui Song
stat.MEmath.STstat.MLarXiv:0903.5255v52009
Orthogonal Gradient Descent for Continual Learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott +1
cs.LGstat.MLarXiv:1910.07104v12019
Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu
cs.LGstat.MLarXiv:1905.02325v22019
CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope
Gautier Marti
stat.MLcs.LGq-fin.RMarXiv:2610.13606v12026
Self-Knowledge Distillation with Progressive Refinement of Targets
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon +1
cs.LGstat.MLarXiv:2006.12000v32020
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
cs.LGcs.AIstat.MLarXiv:1910.06764v12019
Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Xinbing Kong, Zeyu Li, Junfan Mao +1
stat.MLcs.LGecon.EMarXiv:2609.25924v12026
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
Yamato Arai, Yuma Ichikawa
cs.LGstat.APstat.MEarXiv:2504.09629v32025
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
cs.LGcs.DCmath.NAarXiv:1909.04746v42019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Debraj Basu, Deepesh Data, Can Karakus +1
stat.MLcs.DCcs.LGarXiv:1906.02367v22019
Phase Retrieval using Alternating Minimization
Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
stat.MLcs.ITcs.LGarXiv:1306.0160v22013
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Anastasios N. Angelopoulos, Stephen Bates
cs.LGcs.AImath.STarXiv:2107.07511v62021
Free-rider Attacks on Model Aggregation in Federated Learning
Yann Fraboni, Richard Vidal, Marco Lorenzi
cs.LGstat.MLarXiv:2006.11901v52020
High-dimensional nonparametric changepoint detection via low-rank degree-two density projection
Guoqing Zhang, Zhaixin Chen
cs.LGstat.MLarXiv:2608.13922v12026
Collaborative Fairness in Federated Learning
Lingjuan Lyu, Xinyi Xu, Qian Wang
cs.LGcs.DCstat.MLarXiv:2008.12161v22020
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo, Fei Chen, Dapeng Hu +3
cs.LGcs.CVcs.DCarXiv:2106.05001v22021
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona +1
stat.MLq-bio.QMarXiv:1205.1181v12012
Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, Jonas Peters, Dominik Janzing +1
cs.LGstat.MLarXiv:1202.3775v12012
Flow-based Network Traffic Generation using Generative Adversarial Networks
Markus Ring, Daniel Schlör, Dieter Landes +1
cs.NIstat.MLarXiv:1810.07795v12018
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
quant-phcs.LGstat.MLarXiv:2312.09121v32023
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
cs.LGstat.MLarXiv:1906.02319v12019
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle, Yu-Xiang Wang
cs.LGstat.MLarXiv:1805.06530v22018
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber
eess.IVcs.CVcs.LGarXiv:2003.13815v32020
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Andrey Malinin, Neil Band, Ganshin +15
cs.LGcs.AIstat.MLarXiv:2107.07455v32021
Why do classifier accuracies show linear trends under distribution shift?
Horia Mania, Suvrit Sra
cs.LGstat.MLarXiv:2012.15483v22020
Closing the Approximation Gap in Simulation-free Latent SDEs
Henry D. Smith, Brian L. Trippe, Scott W. Linderman
stat.MLcs.LGarXiv:2606.16138v22026
Low-Cost High-Power Membership Inference Attacks
Sajjad Zarifzadeh, Philippe Liu, Reza Shokri
stat.MLcs.CRcs.LGarXiv:2312.03262v32023
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean
stat.MLcs.LGcs.NEarXiv:1503.02531v12015
Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models
Yujie Chen, Antik Chakraborty, Anindya Bhadra
stat.MLcs.LGstat.MEarXiv:2610.09070v12026
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Indro Spinelli, Simone Scardapane, Aurelio Uncini
cs.LGstat.MLarXiv:1905.01907v22019
On the Theory of Transfer Learning: The Importance of Task Diversity
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
cs.LGstat.MLarXiv:2006.11650v22020

Machine Learning (stat)

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Information Theoretic Regret Bounds for Online Nonlinear Control

Residual Flows for Invertible Generative Modeling

FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference

Equivariant Manifold Flows

Learning to Optimize Domain Specific Normalization for Domain Generalization

Wasserstein Weisfeiler-Lehman Graph Kernels

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

Auditing Black-box Models for Indirect Influence

Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data

SLAYER: Spike Layer Error Reassignment in Time

Gaussian Linear Functional Manifold Method for Massive Point Cloud Data

The Optimal Sample Complexity of Multiclass and List Learning

Inferring clonal evolution of tumors from single nucleotide somatic mutations

Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification

Statistical Topic Models for Multi-Label Document Classification

Optimization Methods for Large-Scale Machine Learning

FreDF: Learning to Forecast in the Frequency Domain

Stream Graphs and Link Streams for the Modeling of Interactions over Time

Flexibly Fair Representation Learning by Disentanglement

Graph Neural Networks in TensorFlow and Keras with Spektral

Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Flow Matching for Generative Modeling

Visual Causal Feature Learning

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Sure independence screening in generalized linear models with NP-dimensionality

Orthogonal Gradient Descent for Continual Learning

Sum-of-Squares Polynomial Flow

CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope

Self-Knowledge Distillation with Progressive Refinement of Targets

Stabilizing Transformers for Reinforcement Learning

Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference

Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization

Tighter Theory for Local SGD on Identical and Heterogeneous Data

Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations

Phase Retrieval using Alternating Minimization

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Free-rider Attacks on Model Aggregation in Federated Learning

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

Collaborative Fairness in Federated Learning

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

Kernel-based Conditional Independence Test and Application in Causal Discovery

Flow-based Network Traffic Generation using Generative Adversarial Networks

Does provable absence of barren plateaus imply classical simulability?

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

Why do classifier accuracies show linear trends under distribution shift?

Closing the Approximation Gap in Simulation-free Latent SDEs

Low-Cost High-Power Membership Inference Attacks

Distilling the Knowledge in a Neural Network

Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models

Missing Data Imputation with Adversarially-trained Graph Convolutional Networks

On the Theory of Transfer Learning: The Importance of Task Diversity